Radiation therapy has been utilized to treat tumors in mammalian (e.g., human and animal) tissue. In a radiation therapy treatment session, a high energy beam is applied from an external source towards a patient to produce a collimated beam of radiation directed to a target site of a patient. The placement and dose of the radiation beam must be accurately controlled to ensure that the tumor receives sufficient radiation, and on the other hand, to minimize damage to the surrounding healthy tissue.
One way to improve the accuracy of the beam placement is through image guided radiation therapy process, in which a series of patent images are obtained to aid the application of radiation beams. Physicians can use the patient images to identify a target region (e.g., the tumor) and to identify critical organs near the tumor. They then manually segment the tumor that is to receive a prescribed radiation dose and further segment the critical organs that are at risk of damage from the radiation treatment. Finally, a treatment plan can be created using an optimization technique based on the segmentation of the tumor and the critical organs.
However, reviewing large amount of patent images is a daunting challenge faced by radiation oncologists. For example, a typical radiation oncologist normally reviews images for 20 patients daily. If cone beam CT (CBCT) images are used, the amount of data to be reviewed can exceed 2 GB per day.
The review burden can be lessened by considering that typically greater than 90% of IGRT images reveal no unusual changes, and each image can be quickly assessed before moving on to the next image. Therefore, it would be useful to develop an efficient way to quickly identify the minority of images that warrant a more thorough review.